Sub-Surface Growing and Boundary Generalization for 3d Building Reconstruction

Abstract

ABSTRACT:The automatic reconstruction of 3D building models from airborne point cloud data is still an ongoing research topic. Especially for complex roof shapes, the identification of sub-shapes, the generation of roof boundaries and the construction to well-shaped and topologically correct models remains only partially solved. In this paper, a 3D building reconstruction methodology that is based on the notion of sub-surface growing as a means for point cloud segmentation of planar surfaces is introduced. In contrast to conventional surface growing, the segmentation process continues below other surfaces. As a result, the segments grow larger, their number decrease and the adjacency relations between them become more distinct, thus allowing stricter rules to help identify and differentiate between the root types of the roof sub-shapes and their composition to complex roof structures. In conjunction with a constructive solid modeling approach, the model construction is significantly simplified, as the generation of primitives from subsurface segments is straightforward and their combination to complex shapes can be much easier derived from their interrelations. In the second part of the paper, a boundary generalization approach is presented that allows generating building and segment outlines with regularized shapes from given point sets. Together with sub-surface growing, its usage in the reconstruction of flat roof office buildings is shown. The models are constructed in layers in a bottom-up fashion, each one being the result of a flat sub-surface segment with a generalized boundary, where the regularization rules of one layer are propagated to the next in order to gain wellshaped buildings. A discussion on the so far achieved results and future developments concludes the paper.

Extended Hybrid Region Growing Segmentation of Point Clouds with Different Resolution from Dense Aerial Image Matching

“…Kada et al [8] proposed sub-surface growing to extract complex building roofs as well. In this approach, model-driven and data driven are combined to gain the advantage of both methods.…”

Feature-Driven 3D Building Modeling using Planar Halfspaces

“…The notion of sub-surface segmentation and the algorithm for sub-surface growing are introduced in (Kada and Wichmann, 2012). In summary, the segments of sub-surface segmentation are enlarged with virtual points that geometrically fit the criteria of the segments, but are located below real surface points (cp.…”

“…Regarding the use of thresholds, sub-surface growing allows us to use rather strict thresholds in our feature recognition step (see (Kada and Wichmann, 2012)). And from our experience, they seem to rely more on the size of features as on the point cloud density itself; as long as the point cloud is dense enough to actually represent the feature.…”

“…If the shape is concave, the geometry needs to be decomposed into convex components first. So for a future integration of eaves, we plan to use 2D outlines generated as presented in (Kada and Wichmann, 2012).…”

Automatic Extraction of Building Roof Planes From Airborne Lidar Data Applying an Extended 3d Randomized Hough Transform

“…Vosselman et al (2004) and Sun and Salvaggio (2013) proposed region growing for the segmentation of LIDAR point clouds. Furthermore, interesting and efficient studies have been implemented that segment building roofs (Lafarge et al, 2010;Sampath and Shan, 2010;Kada and Wichmann, 2012;Awrangjeb and Fraser, 2013;Verdie et al, 2015) and man-made scenes (Lafarge and Alliez, 2013;Monszpart et al, 2015). Also, sophisticated techniques have been applied that accurately assign every LIDAR point to its best plane in one global optimization eliminating simultaneously the use of many thresholds (Wang et al, 2012;Lin et al, 2013;Pham et al, 2014;Yan et al, 2014;Golbert et al, 2014).…”